Bi-level heat exchanger network synthesis with evolution method for structure optimization and memetic particle swarm optimization for parameter optimization.

The synthesis of heat exchanger networks (HENs) is a complex problem because of the nonlinearity that results from the integer and continuous variables. Here, a bi-level algorithm for the optimal design of a HEN is proposed that attempts to optimize separately the integer and continuous variables on two levels. The master level is a problem-oriented evolution method generating new candidate HEN structures. The slave level is a memetic particle swarm optimization, an improved particle swarm optimization combined with a local search component, improvement of neighbourhood topologies and control parameter preference. The slave level minimizes the total annual cost (TAC) of a given structure received from the master level, and then sends this value back to the master level for structure evolution. The proposed bi-level method is applied to several cases taken from the literature, which demonstrate its reliable search ability in both structure space and continuous variable space and its ability to optimize the system, producing generally lower TACs than previously used methods. [ABSTRACT FROM AUTHOR]